2025
EMNLP
EMNLP 2025
In2X at WMT25 Translation Task
Abstract
AbstractThis paper presents the open-system submission by the In2x research team for the WMT25 General Machine Translation Shared Task. Our submission focuses on Japanese-related translation tasks, aiming to explore a generalizable paradigm for extending large language models (LLMs) to other languages. This paradigm encompasses aspects such as data construction methods and reward model design. The ultimate goal is to enable large language model systems to achieve exceptional performance in low-resource or less commonly spoken languages.
🌉
Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Resources & Methods > Large Language Models
Natural Language Processing > Generation > Machine Translation
Natural Language Processing > Resources & Methods > Transfer Learning
Deep Learning > Models > Large Language Models